In this paper, we bring the techniques of the Laplacian paradigm to the congested clique, while further restricting ourselves to deterministic algorithms. In particular, we show how to solve a Laplacian system up to precision $\epsilon$ in $n^{o(1)}\log(1/\epsilon)$ rounds. We show how to leverage this result within existing interior point methods for solving flow problems. We obtain an $m^{3/7+o(1)}U^{1/7}$ round algorithm for maximum flow on a weighted directed graph with maximum weight $U$, and we obtain an $\tilde{O}(m^{3/7}(n^{0.158}+n^{o(1)}\text{poly}\log W))$ round algorithm for unit capacity minimum cost flow on a directed graph with maximum cost $W$. Hereto, we give a novel routine for computing Eulerian orientations in $O(\log n \log^* n)$ rounds, which we believe may be of separate interest.
翻译:确定性拥塞团中的拉普拉斯范式
翻译后的摘要:
本文将拉普拉斯范式技术应用于拥塞团,并进一步限制在确定性算法中。我们特别展示了如何在$n^{o(1)}\log(1/\epsilon)$轮内解决拉普拉斯系统以达到精度$\epsilon$。在解决流问题的现有内点方法中,我们展示了如何利用这个结果。在权重有上界$U$的带权指向图上,我们获得了一个$m^{3/7+o(1)}U^{1/7}$轮的最大流算法; 在最大成本不超过$W$的无向图中,我们获得了一个$\tilde{O}(m^{3/7}(n^{0.158}+n^{o(1)}\text{poly}\log W))$轮的最小成本最大流算法。此外,我们还提供了一个在$O(\log n \log^* n)$轮内计算欧拉定向的新例程,我们认为这可能是一个单独的有趣研究方向。